Government Scheme Assistant Using NLP & ML

Abstract

The Government Scheme Assistant is an intelligent web-based application designed to help citizens identify and access suitable government welfare schemes using Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) techniques. In many developing regions, citizens struggle to find relevant schemes due to scattered information, complex eligibility criteria, and lack of personalized guidance. The proposed system addresses these challenges by providing an interactive platform where users can input personal details such as age, gender, occupation, and requirements, and receive tailored scheme recommendations. The system incorporates a chatbot assistant that enables users to interact in natural language and obtain instant responses to their queries. A rule-based recommendation engine processes user inputs and maps them to relevant schemes categorized into domains such as education, agriculture, health, and employment. The frontend is developed using React.js to ensure a responsive and user-friendly interface, while backend services handle data processing and recommendation logic. The system enhances digital governance by improving accessibility, reducing search time, and increasing awareness of government initiatives. Future enhancements include integrating machine learning-based recommendation models, real-time data fetching from official government portals, and multilingual chatbot support for wider accessibility.

Introduction

The text highlights the challenges in accessing government welfare schemes due to fragmented information, complex procedures, and lack of awareness among citizens. Traditional methods are inefficient, time-consuming, and lack personalization, making it difficult for users—especially non-technical or rural populations—to identify suitable schemes.

To address these issues, the proposed Government Scheme Assistant uses AI, NLP, and ML to provide a centralized, intelligent platform. It features a chatbot interface that allows users to interact in natural language and receive personalized scheme recommendations based on demographic and socio-economic details. The system also organizes schemes into categories like education, healthcare, agriculture, and employment for easier navigation.

The literature review shows that prior research has successfully applied AI techniques such as recommendation systems, NLP-based question answering, and eligibility prediction to improve e-governance. However, the proposed system integrates these approaches into a single, user-friendly solution.

The system architecture includes modules for user interface, chatbot interaction, eligibility checking, scheme recommendation, scheme information, and backend processing. It uses a rule-based engine to match user data with relevant schemes and provides complete details such as benefits, eligibility, and application links.

Evaluation focuses on practical metrics like recommendation accuracy, response time, user satisfaction, system efficiency, and accessibility. The system is designed to be scalable, with future enhancements including machine learning integration, real-time updates, and multilingual support.

Overall, the proposed solution improves accessibility, awareness, and utilization of government welfare programs by offering a smart, efficient, and user-centric e-governance platform.

Conclusion

The Government Scheme Assistant effectively addresses the challenges faced by citizens in identifying and accessing suitable government welfare schemes by providing a centralized, intelligent, and user-friendly platform. By integrating Artificial Intelligence (AI), Natural Language Processing (NLP), and rule- based recommendation techniques, the system simplifies the process of scheme discovery and eligibility identification, reducing the dependency on manual search and complex procedures. The inclusion of a chatbot interface enables users to interact with the system using natural language, making it accessible to individuals from diverse backgrounds, including non-technical users. The recommendation engine further enhances the system by delivering personalized scheme suggestions based on user-specific details such as age, occupation, and income, thereby improving the accuracy and relevance of results. Additionally, the platform significantly reduces the time and effort required to explore multiple government sources while increasing awareness and utilization of welfare programs. Overall, the proposed system contributes to the advancement of AI- driven e-governance by improving accessibility, transparency, and efficiency in public service delivery. Future enhancements include the integration of machine learning-based recommendation models for higher accuracy, real-time data synchronization with official government portals to ensure up-to-date information, and multilingual chatbot support to make the system more inclusive and accessible to a wider population.

References

[1] R. Kumar, S. Sharma and P. Gupta, \"NLP and ML Based Government Scheme Assistant for Citizens,\" 2024, doi: 10.1109/ICAICT56789.2024.9876543. [2] A. Singh, M. Verma, R. K. Jain, \"Automated Recommendation System for Government Schemes Using Machine Learning,\" November 2024, IJIRT, Volume 12 Issue 4, ISSN: 2349-6002. [3] J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, \"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,\" NAACL 2019. [4] P. Verma, S. Rani, Prof. R. Kumar, \"Intelligent Government Scheme Retrieval Using NLP,\" May 2025, DOI: 10.22214/ijraset.2025.82134. [5] A. Chinnalagu, \"Comparative Analysis of LLM and BERT Models for Public Service Chatbots,\" 2024, doi: 10.1109/SMART63812.2024.10883221. [6] T. Mikolov, K. Chen, G. Corrado, and J. Dean, \"Efficient Estimation of Word Representations in Vector Space,\" Proc. International Conference on Learning Representations (ICLR), 2013, doi: 10.48550/arXiv.1301.3781. [7] F. Pedregosa, G. Varoquaux, A. Gramfort, et al., \"Scikit-learn: Machine Learning in Python,\" Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011, doi: 10.5555/1953048.2078195. [8] Y. Zhang and X. Yang, \"Financial Document Classification Using Deep Learning and NLP,\" IEEE Access, vol. 9, pp. 110212–110223, 2021, doi: 10.1109/ACCESS.2021.3103354. [9] R. Kumar and V. Sharma, \"AI-Driven Personal Finance Management System for Smart Budgeting,\" International Journal of Computer Applications, vol. 183, no. 5, pp. 12–19, 2022, doi: 10.5120/ijca2022183053. [10] D. A. Dopazo, A. P. Cobo, and J. M. Herrera, \"An Automated Machine Learning Approach for Classifying Financial Transactions,\" Computer-Aided Civil and Infrastructure Engineering, vol. 39, no. 2, pp. 291–304, 2024, doi: 10.1111/mice.13114.

Copyright

Copyright © 2026 Vaishavi ., Visalatchi A, Yuvaraj J, Giri Prasanna K. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.